Red flags that you are thinking of data incorrectly

Thinking that your company is sitting on a gold mine of data. C is just noise. Developing insights requires a thorough understanding of the business.

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Thinking that your company is sitting on a gold mine of data

C is just noise. Developing insights requires a thorough understanding of the business. This understanding depends on having the right visibility into what’s actually happening with the business and a well crafted data collection layer.
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Treating the data team as a black box

Data is a team sport. The data team is not going to be able to change the company culture alone, this requires executive sponsorship and proactive communication. Remember it takes effort to set an object in motion. Data is no different.
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Making decisions based on hunches

Using data reporting to help lead business decisions starts at the top. This means that as a leader if you do not lead by example your org is going to have mixed signals. Nothing undervalues a data team more than having the correct reporting but not using it.
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Not investing in building the right data infrastructure

Trying to build a data driven company without investing in building out the right infrastructure is like trying to build a skyscraper without digging up the ground to build a foundation. You can’t build a long term scalable data engine without upfront work needed to ingest, model and visualize data.
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Investing in data science without having a quality data

Turns out that data scientists end up spending the majority of their time collecting and cleaning data instead of building models. Remember data science is at the top of the data pyramid you need to have the right infrastructure and data reporting layer to be able to get started on ML work.
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Using data to prove instead of learn

You can use data to prove a point or to learn from. There is a place for both. However if you start to only use data to prove a point you are missing out on continuing to evolve your understanding of the business and you will be left behind.

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